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Applied Sciences
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Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance
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Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 5256

Special Issue Editors

Dr. Zhipeng Wang

E-Mail Website
Guest Editor
State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Interests: UAV-based automatic railway inspection; fault diagnosis; prognostics and health management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yong Qin

E-Mail Website
Guest Editor
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
Interests: intelligent perception and fault diagnosis; risk analysis and emergency command; image intelligent analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Geng

E-Mail Website
Guest Editor
School of Reliability and Systems engineering, Beihang University, Beijing 100191, China
Interests: reliability engineering; maintenance; maintainability analysis; virtual maintenance; maintenance strategy

Special Issue Information

Dear Colleagues,

In the past few decades, as an important industry to promote economic development and regional exchanges, railways, including high-speed railways, have undergone a considerable period of large-scale construction and have grown significantly. However, since the railway networks in operation become more and more complex, a great challenge is to perform efficient health monitoring, assessment and maintenance of large-scale railway infrastructures in order to ensure the safe operation and sustainable use of railways. In recent years, with the development of various dynamic monitoring and sensing methods, it is urgent to integrate these new technologies into the railway field to improve the existing railway safety assurance capabilities. At the same time, in the context of artificial intelligence and big data, a large amount of data accumulated in the long-term railway operation process can be fully mined to provide stronger support for future railway safety operations.

This Special Issue aims to review the latest research progress and advanced engineering applications in the field of railway safety operation. Submissions of conceptual, empirical and literature review papers focusing on this field are all encouraged. Different types and algorithms in this area are welcome in this Special Issue.

Dr. Zhipeng Wang
Prof. Dr. Yong Qin
Dr. Jie Geng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • railway infrastructures
  • fault diagnosis
  • health monitoring
  • health assessment
  • maintenance
  • reliability
  • automatic inspection

Published Papers (6 papers)

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Research

14 pages, 4591 KiB  
Article
Experimental Study and Phenomenological Laws of Some Nonlinear Behaviours of the Wheel–Rail Contact Associated with the Deshunting Phenomenon
by Guy-Léon Kaza, Frédéric Houzé, Florent Loëte and Philippe Testé
Appl. Sci. 2023, 13(21), 11752; https://doi.org/10.3390/app132111752 - 27 Oct 2023
Viewed by 713
Abstract
A widely used technique for locating the position of a train during its journey involves electrical detection: on a section of track, the train closes (“shunts”) a circuit dedicated to its location via its wheels and axles. The quality of the wheel–rail contact [...] Read more.
A widely used technique for locating the position of a train during its journey involves electrical detection: on a section of track, the train closes (“shunts”) a circuit dedicated to its location via its wheels and axles. The quality of the wheel–rail contact is therefore particularly important for signalling management and traffic control. In this paper, we show that the current flowing through the track circuit induces a permanent modification and a nonlinear, frequency-dependent behaviour of the electrical contact. We propose an analytical expression that describes the evolution of the measured voltage as a function of current and frequency for these nonlinear behaviours. This behavioural law was obtained using experimental measurements on a real train and could therefore be integrated into a global model describing the track system. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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16 pages, 5354 KiB  
Article
Quality Behaviour of Turnouts: Comparison, Problem Specification and Recommendation of Measures
by Markus Loidolt, Stefan Marschnig, Maximilian Bürgler, Armin Berghold, Peter Dornig and Uwe Ossberger
Appl. Sci. 2023, 13(19), 10665; https://doi.org/10.3390/app131910665 - 25 Sep 2023
Viewed by 568
Abstract
For future requirements, asset management of turnouts needs to rely on data-based assessment tools. These tools must enable the quantification of quality behaviour of turnouts and identify causes of poor behaviour. In this paper, we provide a toolbox addressing these requirements. We use [...] Read more.
For future requirements, asset management of turnouts needs to rely on data-based assessment tools. These tools must enable the quantification of quality behaviour of turnouts and identify causes of poor behaviour. In this paper, we provide a toolbox addressing these requirements. We use track geometry as the main criterion for quality behaviour in combination with additional indicators, each associated with a different component, to understand turnout performance. The toolbox is applied to five similar turnouts to compare their performance. It is revealed that one of the turnouts performs significantly worse than the others. A deeper analysis can identify worn ballast in several areas of the turnout as the cause of poor performance. Problems in the ballast bed can be attributed to worn insulated rail joints as well as to stiffness changes in the transition areas of the turnout. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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15 pages, 7645 KiB  
Article
Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
by Meng He, Wang Qing and Jiantao Qu
Appl. Sci. 2023, 13(17), 9663; https://doi.org/10.3390/app13179663 - 26 Aug 2023
Viewed by 591
Abstract
Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged [...] Read more.
Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed a good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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14 pages, 14924 KiB  
Article
Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
by Manuel Krammer, Markus Pröll, Martin Bürger and Gerald Zauner
Appl. Sci. 2023, 13(15), 8678; https://doi.org/10.3390/app13158678 - 27 Jul 2023
Viewed by 1049
Abstract
This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding [...] Read more.
This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding the rail manufacturer, steel quality, year of production, and rail profile. As a result, they empower rail infrastructure managers to gain valuable insights into their infrastructure. The rolling marks were captured using a standard color camera under dark field illumination. The recognition of individual numbers, letters, and special characters was achieved through state-of-the-art deep neural network object detection, specifically employing the YOLO architecture. By leveraging reference rolling marks, the detected characters can then be accurately interpreted and corrected. This correction process involves calculating a weighted Levenshtein distance, ensuring that the system can identify and rectify partially misidentified rolling marks. Through the proposed system, the accurate and reliable identification of rolling marks was achieved, even in cases in which there were partial errors in the detection process. This novel system thus has the potential to substantially improve the management and maintenance of railroad infrastructure. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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17 pages, 3329 KiB  
Article
Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
by Mengwei Li, Ying Zhou, Limin Jia, Yong Qin and Zhipeng Wang
Appl. Sci. 2023, 13(14), 8226; https://doi.org/10.3390/app13148226 - 15 Jul 2023
Cited by 1 | Viewed by 710
Abstract
A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is [...] Read more.
A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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22 pages, 2500 KiB  
Article
Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
by Jie Geng, Ying Li, Hailong Guo, Huan Zhang and Chuan Lv
Appl. Sci. 2022, 12(24), 12804; https://doi.org/10.3390/app122412804 - 13 Dec 2022
Viewed by 793
Abstract
Reachability, as a vital parameter in product maintainability design, exerts a tremendous influence in practical maintenance, especially in the usage stage. To decrease subjectivity in maintenance reachability analysis, this study proposes an automatic and quantitative approach based on the spatial data of the [...] Read more.
Reachability, as a vital parameter in product maintainability design, exerts a tremendous influence in practical maintenance, especially in the usage stage. To decrease subjectivity in maintenance reachability analysis, this study proposes an automatic and quantitative approach based on the spatial data of the human arm to implement maintenance reachability analysis. The approach focused on two aspects, namely, accuracy and efficiency. In terms of accuracy, the presented methodology starts from the maintenance spot where the human hand is attached. An original global data sequence set was generated, including the wrist, elbow, and shoulder joints, under the constraints of kinematics, in which a data sequence represents an arm motion. Moreover, the surrounding objects are represented by their geometric data, in which each data sequence is analyzed to judge whether collision occurs between arm segments and surrounding objects. In this filtering process, the data sequence is retained if the aforementioned collision does not occur. In terms of efficiency, owing to the large number of global data sequences, the efficiency of the interval selection in collision calculation is also taken into consideration in this methodology. Unlike the traditional methods in the virtual environment, the starting point is the maintenance spot, rather than the human body. Hence, nearly all possibilities of arm postures are considered in a global perspective with little subjective involvement, which enhances the automation and objectivity in maintenance reachability analysis to a certain extent. The case study shows the usability and feasibility by a practical maintenance scene. Full article
(This article belongs to the Special Issue Sustainable Railway Infrastructures: Health Monitoring, Assessment and Maintenance)
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